Nonparametric hierarchical Bayes via sequential imputations

Abstract

We consider the empirical Bayes estimation of a distribution using binary data via the Dirichlet process. Let $\mathscr{D}(\alpha)$ denote a Dirichlet process with $\alpha$ being a finite measure on Instead of having direct samples from an unknown random distribution F from $\mathscr{D}(\alpha)$, we assume that only indirect binomial data are observable. This paper presents a new interpretation of Lo's formula, and thereby relates the predictive density of the observations based on a Dirichlet process model to likelihoods of much simpler models. As a consequence, the log-likelihood surface, as well as the maximum likelihood estimate of $c = \alpha([0, 1])$, is found when the shape of $\alpha$ a is assumed known, together with a formula for the Fisher information evaluated at the estimate. The sequential imputation method of Kong, Liu and Wong is recommended for overcoming computational difficulties commonly encountered in this area. The related approximation formulas are provided. An analysis of the tack data of Beckett and Diaconis, which motivated this study, is supplemented to illustrate our methods.